Best R Packages for Large Spatial Data Intersections

When working with large multipolygon shapefiles in R, the sf package can indeed struggle with performance and memory management. Based on current research and practical experience, several efficient alternatives and techniques can help you handle these spatial operations more effectively.

Contents

• terra Package as Primary Alternative
• Performance Optimization Techniques
• Geometry Validation and Error Handling
• Memory Management Strategies
• Advanced Processing Approaches
• Comparative Analysis of Alternatives
• Practical Implementation Recommendations

terra Package as Primary Alternative

The terra package emerges as the most promising alternative to sf for large multipolygon operations. Unlike sf, terra implements a more efficient memory management approach where files reside as pointers and are only loaded into RAM as needed for calculations source.

Key advantages include:

• Reduced memory footprint: Terra’s vect() function handles large files without loading entire datasets into memory
• Faster intersection operations: The intersect() function for SpatVectors outperforms sf’s st_intersection() for large datasets
• Better error handling: More robust geometry processing reduces common intersection errors

# Basic terra workflow for large multipolygon intersection
library(terra)

# Load data using terra's efficient memory management
shp1 <- vect("path/to/large_shapefile1.shp")
shp2 <- vect("path/to/large_shapefile2.shp")

# Perform intersection
result <- intersect(shp1, shp2)

According to the terra documentation, the package specifically supports “processing of very large files” and provides improved performance over previous spatial packages.

Performance Optimization Techniques

Spatial Indexing and Pre-filtering

sf package has recently incorporated spatial indexes that can significantly improve performance for binary geometry operations source. You can leverage this even when primarily using sf:

# Add spatial index to improve performance
sf_object <- st_sf(sf_object)
st_index(sf_object)

# Use st_filter() first to identify potential intersections before full intersection
potential_intersections <- st_filter(sf_object1, sf_object2)
full_intersection <- st_intersection(potential_intersections, sf_object2)

Parallel Processing

For operations that can be parallelized, consider using parallel processing:

library(parallel)

# Set up parallel cluster
cl <- makeCluster(detectCores())

# Export necessary data to cluster nodes
parLapply(cl, list(sf_object1, sf_object2), function(x) clusterExport(cl, "x"))

# Process in parallel
results <- parLapply(cl, chunk_list, function(chunk) {
  st_intersection(chunk, sf_object2)
})

stopCluster(cl)

Geometry Validation and Error Handling

The geometry errors you’re encountering can be addressed through proper validation and cleaning:

Using s2 Package for Validation

The s2 package provides more robust geometry validation than sf’s built-in methods:

library(s2)

# Validate geometries before processing
valid_geometries <- s2_is_valid_detail(sf_object$geometry)
sf_object <- sf_object[valid_geometries, ]

# Or fix invalid geometries
sf_object$geometry <- s2_make_valid(sf_object$geometry)

Progressive Geometry Cleaning

Instead of attempting to clean all geometries at once, implement a progressive approach:

# Identify and clean problematic geometries step by step
invalid_indices <- which(!s2_is_valid_detail(sf_object$geometry))

# Process in batches of invalid geometries
batch_size <- 100
for(i in seq(1, length(invalid_indices), batch_size)) {
  batch <- invalid_indices[i:(i+batch_size-1)]
  sf_object$geometry[batch] <- s2_make_valid(sf_object$geometry[batch])
  
  # Save progress
  write_sf(sf_object, paste0("progress_", i, ".gpkg"))
}

Memory Management Strategies

Data Subsetting and Chunking

While you mentioned chunking isn’t practical, strategic subsetting can help:

# Create extent-based subsets
ext <- st_as_sfc(st_bbox(sf_object1))
subsets <- st_intersection(sf_object2, st_as_sf(ext))

# Process each subset independently
results <- lapply(subsets, function(subset) {
  st_intersection(sf_object1, subset)
})

Efficient Data Types

Convert single polygons to multipolygons where possible:

# Convert POLYGON to MULTIPOLYGON for consistency
sf_object$geometry <- st_cast(sf_object$geometry, "MULTIPOLYGON")

Advanced Processing Approaches

Hybrid sf-terra Approach

Combine the strengths of both packages:

library(sf)
library(terra)

# Use terra for initial intersection detection
terra_shp1 <- vect(sf_object1)
terra_shp2 <- vect(sf_object2)

# Quick intersection check
intersects <- intersects(terra_shp1, terra_shp2)

# Convert back to sf for detailed processing
sf_intersects <- st_as_sf(terra_shp1[intersects, ])
sf_result <- st_intersection(sf_intersects, sf_object2)

Database-Driven Processing

Consider using spatial databases like PostGIS for extremely large datasets:

# Export to PostGIS
st_write(sf_object1, "PG:dbname=yourdb user=youruser", layer="layer1")
st_write(sf_object2, "PG:dbname=yourdb user=youruser", layer="layer2")

# Use SQL for intersection
db <- dbConnect(RPostgres::Postgres(), dbname="yourdb", user="youruser")
result <- dbGetQuery(db, "
  SELECT * 
  FROM layer1 A, layer2 B 
  WHERE ST_Intersects(A.geom, B.geom)
")

Comparative Analysis of Alternatives

The terra package documentation explicitly states it’s “faster and easier to use” than the raster package it replaces, making it particularly suitable for your use case.

Practical Implementation Recommendations

Recommended Workflow

Based on the research findings, here’s an optimized workflow:

1. Initial Data Preparation

library(terra)

# Load data with terra's efficient memory management
shp1 <- vect("large_file1.shp")
shp2 <- vect("large_file2.shp")

# Validate geometries using s2
if(any(!s2_is_valid_detail(st_as_sfc(shp1)))){
  shp1 <- st_as_sf(s2_make_valid(st_as_sfc(shp1)))
}
if(any(!s2_is_valid_detail(st_as_sfc(shp2)))){
  shp2 <- st_as_sf(s2_make_valid(st_as_sfc(shp2)))
}

2. Optimized Intersection

# Use terra's intersect function
result <- intersect(shp1, shp2)

# If need sf output
result_sf <- st_as_sf(result)

3. Fallback Strategy

# If terra fails, try optimized sf approach with spatial indexing
library(sf)

sf1 <- st_read("large_file1.shp")
sf2 <- st_read("large_file2.shp")

# Add spatial index
st_index(sf1)
st_index(sf2)

# Pre-filter to reduce computational load
potential_intersections <- st_filter(sf1, sf2)
final_result <- st_intersection(potential_intersections, sf2)

Performance Monitoring

Implement monitoring to identify bottlenecks:

library(pryr)

# Monitor memory usage
mem_change <- mem_change({
  result <- intersect(shp1, shp2)
})

# Monitor execution time
system.time({
  result <- intersect(shp1, shp2)
})

Sources

1. Stack Overflow - Intersecting very large shape files with thousands of multi-polygons
2. terra package documentation - CRAN
3. terra package description - CRAN
4. sf package NEWS - Performance improvements
5. GIS Stack Exchange - Speeding up intersection of large shapefiles
6. terra intersect function documentation
7. Introduction to Spatial Data - sf and terra comparison

Conclusion

Based on comprehensive research into R’s spatial processing capabilities, here are the key takeaways for handling large multipolygon shapefiles:

1. terra package is the top alternative to sf for large spatial datasets, offering superior memory management and faster performance through lazy loading of data.

2. Geometry validation is crucial - use the s2 package for more robust validation than sf’s built-in methods to prevent the specific errors you’re encountering.

3. Spatial indexing can significantly improve performance even in sf, reducing computational complexity for intersection operations.

4. Hybrid approaches combining terra and sf packages can leverage the strengths of both systems for optimal results.

5. Progressive processing strategies help manage memory usage and provide recovery points when dealing with extremely large datasets.

For your specific case with 200MB-1GB shapefiles containing up to 20,000 features, I recommend starting with terra package implementation, as the research consistently shows it provides the best balance of performance, memory efficiency, and reliability for large-scale spatial operations.